Javad Rafiee
University of Tulsa
26 Papers
21 Citations
Javad Rafiee is an academic researcher from University of Tulsa. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 4, co-authored 7 publications. Previous affiliations of Javad Rafiee include Sharif University of Technology.
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Papers
Theoretical and efficient practical procedures for the generation of inflation factors for ES-MDA
Javad Rafiee,Albert C. Reynolds +1 more
Abstract: The ensemble smoother with multiple data assimilation (ES-MDA) has proved to be a powerful assisted history-matching method. The main drawback of ES-MDA is that the inflation factors for damping the changes in model parameters have to be determined before starting the history-match. Although various authors have provided suggestions for determining the inflation factors adaptively as the history-match proceeds, these methods often result in a large number of data assimilation steps which can make ES-MDA too computationally inefficient for practical application to large-scale field problems. Here, we provide a theoretical procedure to determine exactly the minimum inflation factor at each data assimilation step that ensures the discrepancy principle is satisfied. Like previous adaptive ES-MDA methods, this method does not allow one to specify a priori the number of data assimilation steps to be done. Thus, using the exact theoretical procedure as a guide, we provide a practical efficient method for determining the inflation factors which allows one to specify a priori the number of data assimilation steps to be done with ES-MDA which still ensures that the initial inflation factor is chosen so that the discrepancy principle is approximately satisfied.
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A Two-Level MCMC Based On The Distributed Gauss-Newton Method For Uncertainty Quantification
Javad Rafiee,Albert C. Reynolds +1 more
- 03 Sep 2018
TL;DR: A two-level MCMC procedure which can sample multimodal posteriors relatively efficiently and is far more efficient than the random-walk MCMC is developed and applied.
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A Bayesian model selection analysis of equilibrium and nonequilibrium models for multiphase flow in porous media
TL;DR: In this paper, the authors apply a Bayesian model selection framework in order to examine the relative efficacy of these three models to represent experimental observations and conclude that Barenblatt's nonequilibrium model is more likely to match data from unstable displacements that involve higher viscosity ratios of the invading phase to the resident fluid.
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Neural network prediction model of three-phase fluids flow in heterogeneous porous media using scaling analysis
TL;DR: In this article, the scaling studies of multiphase fluid flow through permeable media with a special attention to the three-phase immiscible water alternating gas (WAG) flooding under conditions prevailing in many oil reservoirs were performed on a heterogeneous reservoir to study in detail the sensitivity of the displacement process to the scaling groups using various combinations of the process controlling parameters.
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Combining Machine Learning and Physics for Robust Optimization of Completion Design and Well Location of Unconventional Wells
Javad Rafiee,Pallav Sarma,Yong Zhao,Sebastian Plotno,C. Calad,Dayanara Betancourt +5 more
- 21 Feb 2022
TL;DR: The proposed model is the amalgamation of the state-of-the-art in machine learning and reservoir physics into a seamless full field model for design, prediction and optimization of unconventional wells efficiently using a combination of reservoir physics with machine learning methodologies.
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